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Register a new userSuperconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory
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We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through 5-passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level RF system configured such that a cavity fault triggers waveform recordings of 17 RF signals for each of the 8 cavities in the cryomodule. Subject matter experts (SME) are able to analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time (rather than post-mortem) identification of the offending cavity and classification of the fault type has been implemented. We discuss performance of the ML models during a recent physics run. Results show the cavity identification and fault classification models have accuracies of 84.9% and 78.2%, respectively.
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Recently, heat treatment between 250 C and 500 C has been attempted to improve quality factor of superconducting radio-frequency cavities at FNAL and KEK. Experiments of such medium temperature (mid-T) bake with furnaces have also been carried out at IHEP. Firstly, eleven 1.3 GHz 1-cell cavities were treated with different temperatures at a small furnace. The average quality factor has reached 3.6E10 when the gradient is 16 MV/m. Then, the recipe of mid-T furnace bake at 300 C for 3 hours has been applied to six 1.3 GHz 9-cell cavities at a new big furnace. The average quality factor has reached 3.8E10 when the gradient is 16 MV/m.
We report the rf performance of a single-cell superconducting radiofrequency cavity after low temperature baking in a nitrogen environment. A significant increase in quality factor has been observed when the cavity was heat treated in the temperature range of 120-160 {deg}C with a nitrogen partial pressure of ~25 mTorr. This increase in quality factor as well as the Q-rise phenomenon (anti-Q-slope) is similar to those previously obtained with high temperature nitrogen doping as well as titanium doping. In this study, a cavity N2-treated at 120 {deg}C and at140 {deg}C, showed no degradation in accelerating gradient, however the accelerating gradient was degraded by 25 with a 160 {deg}C N2 treatment. Sample coupons treated in the same conditions as the cavity were analyzed by scanning electron microscope, x-ray photoelectron spectroscopy and secondary ion mass spectroscopy revealed a complex surface composition of Nb_2O5, NbO and NbN(1-x)Ox within the rf penetration depth. Furthermore, magnetization measurements showed no significant change on bulk superconducting properties.
Baryons are complex systems of confined quarks and gluons and exhibit the characteristic spectra of excited states. The systematics of the baryon excitation spectrum is important to our understanding of the effective degrees of freedom underlying nucleon matter. High-energy electrons and photons are a remarkably clean probe of hadronic matter, providing a microscope for examining the nucleon and the strong nuclear force. Current experimental efforts with the CLAS spectrometer at Jefferson Laboratory utilize highly-polarized frozen-spin targets in combination with polarized photon beams. The status of the recent double-polarization experiments and some preliminary results are discussed in this contribution.
A buncher cavity has been developed for the muons accelerated by a radio-frequency quadrupole linac (RFQ). The buncher cavity is designed for $beta=v/c=0.04$ at an operational frequency of 324 MHz. It employs a double-gap structure operated in the TEM mode for the required effective voltage with compact dimensions, in order to account for the limited space of the experiment. The measured resonant frequency and unloaded quality factor are 323.95 MHz and $3.06times10^3$, respectively. The buncher cavity was successfully operated for longitudinal bunch size measurement of the muons accelerated by the RFQ.
Data-driven fault classification is complicated by imbalanced training data and unknown fault classes. Fault diagnosis of dynamic systems is done by detecting changes in time-series data, for example residuals, caused by faults or system degradation. Different fault classes can result in similar residual outputs, especially for small faults which can be difficult to distinguish from nominal system operation. Analyzing how easy it is to distinguish data from different fault classes is crucial during the design process of a diagnosis system to evaluate if classification performance requirements can be met. Here, a data-driven model of different fault classes is used based on the Kullback-Leibler divergence. This is used to develop a framework for quantitative fault diagnosis performance analysis and open set fault classification. A data-driven fault classification algorithm is proposed which can handle unknown faults and also estimate the fault size using training data from known fault scenarios. To illustrate the usefulness of the proposed methods, data have been collected from an engine test bench to illustrate the design process of a data-driven diagnosis system, including quantitative fault diagnosis analysis and evaluation of the developed open set fault classification algorithm.
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